Performance

Although it was years ago, I still remember the conversation vividly. I was having my regular check-in with the CTO. It was also sunny outside, and the view from his office was particularly nice that day.

He asked me, “Is our app scalable?” After a moment of thought, I answered, “Yes.”

I spent the next few years remembering that conversation, wondering what would have happened if I had asked what he meant by scalable.

I spent just shy of 9 years working on a suite of products that stored and processed environmental sensor data. This kind of data is naturally stored and processed as a time series. A time series is a set of data points, each with a time and value, and often more. They are common for sensor data, but can be used for pretty much anything that is measured over time such as prices, performance metrics, and analytical data.

I’ve spent the last several years working on multi-user server systems in two different companies. Both those companies had a giant monitor hanging off a wall showing a graph of connected users. It won’t give you detailed diagnostic information, but it is a good indicator for the health of your servers, and your product generally. If you learn to notice certain patterns in your user graph, it can also save you precious time when things go wrong.

Or: The Most Impressive T-SQL Script I’ve Ever Written

I was recently working on a new application. After three months in the field, users were starting to complain about performance issues. We had done some limited performance tuning for the first release, and more as part of the second release, but new issues were popping up as more data got entered into the system. We could have continued fixing issues as they came up, one release at a time, but we wanted to get ahead of the problem, and the client wanted to know that the system would remain usable without developer intervention for a few years at least.